Entanglement Classification via Neural Network Quantum States

Cillian Harney, Stefano Pirandola, Alessandro Ferraro, Mauro Paternostro

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
32 Downloads (Pure)


The task of classifying the entanglement properties of a multipartite quantum state poses a remarkable challenge due to the exponentially increasing number of ways in which quantum systems can share quantum correlations. Tackling such challenge requires a combination of sophisticated theoretical and computational techniques. In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS), whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States (SNNS) can be used to build entanglement witnesses for any target state.
Original languageEnglish
Article number045001
Number of pages13
JournalNew Journal of Physics
Publication statusPublished - 02 Apr 2020


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